<a href="https://apps.apple.com/app/id1452689527" target="_blank">
<img src="https://user-images.githubusercontent.com/26833433/98699617-a1595a00-2377-11eb-8145-fc674eb9b1a7.jpg" width="1000"></a>
 
<a href="https://github.com/ultralytics/yolov5/actions"><img src="https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg" alt="CI CPU testing"></a>
This repository represents Ultralytics open-source research into future object detection methods, and incorporates lessons learned and best practices evolved over thousands of hours of training and evolution on anonymized client datasets. **All code and models are under active development, and are subject to modification or deletion without notice.** Use at your own risk.
<p align="center"><img width="800" src="https://user-images.githubusercontent.com/26833433/114313216-f0a5e100-9af5-11eb-8445-c682b60da2e3.png"></p>
<details>
<summary>YOLOv5-P5 640 Figure (click to expand)</summary>
<p align="center"><img width="800" src="https://user-images.githubusercontent.com/26833433/114313219-f1d70e00-9af5-11eb-9973-52b1f98d321a.png"></p>
</details>
<details>
<summary>Figure Notes (click to expand)</summary>
* GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS.
* EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8.
* **Reproduce** by `python test.py --task study --data coco.yaml --iou 0.7 --weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
</details>
- **April 11, 2021**: [v5.0 release](https://github.com/ultralytics/yolov5/releases/tag/v5.0): YOLOv5-P6 1280 models, [AWS](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart), [Supervise.ly](https://github.com/ultralytics/yolov5/issues/2518) and [YouTube](https://github.com/ultralytics/yolov5/pull/2752) integrations.
- **January 5, 2021**: [v4.0 release](https://github.com/ultralytics/yolov5/releases/tag/v4.0): nn.SiLU() activations, [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme) logging, [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/) integration.
- **August 13, 2020**: [v3.0 release](https://github.com/ultralytics/yolov5/releases/tag/v3.0): nn.Hardswish() activations, data autodownload, native AMP.
- **July 23, 2020**: [v2.0 release](https://github.com/ultralytics/yolov5/releases/tag/v2.0): improved model definition, training and mAP.
## Pretrained Checkpoints
[assets]: https://github.com/ultralytics/yolov5/releases
Model |size<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>test<br>0.5:0.95 |mAP<sup>val<br>0.5 |Speed<br><sup>V100 (ms) | |params<br><sup>(M) |FLOPS<br><sup>640 (B)
--- |--- |--- |--- |--- |--- |---|--- |---
[YOLOv5s][assets] |640 |36.7 |36.7 |55.4 |**2.0** | |7.3 |17.0
[YOLOv5m][assets] |640 |44.5 |44.5 |63.3 |2.7 | |21.4 |51.3
[YOLOv5l][assets] |640 |48.2 |48.2 |66.9 |3.8 | |47.0 |115.4
[YOLOv5x][assets] |640 |**50.4** |**50.4** |**68.8** |6.1 | |87.7 |218.8
| | | | | | || |
[YOLOv5s6][assets] |1280 |43.3 |43.3 |61.9 |**4.3** | |12.7 |17.4
[YOLOv5m6][assets] |1280 |50.5 |50.5 |68.7 |8.4 | |35.9 |52.4
[YOLOv5l6][assets] |1280 |53.4 |53.4 |71.1 |12.3 | |77.2 |117.7
[YOLOv5x6][assets] |1280 |**54.4** |**54.4** |**72.0** |22.4 | |141.8 |222.9
| | | | | | || |
[YOLOv5x6][assets] TTA |1280 |**55.0** |**55.0** |**72.0** |70.8 | |- |-
<details>
<summary>Table Notes (click to expand)</summary>
* AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results denote val2017 accuracy.
* AP values are for single-model single-scale unless otherwise noted. **Reproduce mAP** by `python test.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
* Speed<sub>GPU</sub> averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) V100 instance, and includes FP16 inference, postprocessing and NMS. **Reproduce speed** by `python test.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45`
* All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).
* Test Time Augmentation ([TTA](https://github.com/ultralytics/yolov5/issues/303)) includes reflection and scale augmentation. **Reproduce TTA** by `python test.py --data coco.yaml --img 1536 --iou 0.7 --augment`
</details>
## Requirements
Python 3.8 or later with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) dependencies installed, including `torch>=1.7`. To install run:
```bash
$ pip install -r requirements.txt
```
## Tutorials
* [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) ð RECOMMENDED
* [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results) âï¸ RECOMMENDED
* [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289) ð NEW
* [Supervisely Ecosystem](https://github.com/ultralytics/yolov5/issues/2518) ð NEW
* [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
* [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) â NEW
* [ONNX and TorchScript Export](https://github.com/ultralytics/yolov5/issues/251)
* [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303)
* [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318)
* [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304)
* [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607)
* [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314) â NEW
* [TensorRT Deployment](https://github.com/wang-xinyu/tensorrtx)
## Environments
YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):
- **Google Colab and Kaggle** notebooks with free GPU: <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)
- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)
- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
## Inference
`detect.py` runs inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.
```bash
$ python detect.py --source 0 # webcam
file.jpg # image
file.mp4 # video
path/ # directory
path/*.jpg # glob
'https://youtu.be/NUsoVlDFqZg' # YouTube video
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
```
To run inference on example images in `data/images`:
```bash
$ python detect.py
没有合适的资源?快使用搜索试试~ 我知道了~
YOLOv5算法DMS驾驶员抽烟-打电话-喝水-吃东西分神检测+数据集
共2000个文件
txt:1999个
md:1个
1.该资源内容由用户上传,如若侵权请联系客服进行举报
2.虚拟产品一经售出概不退款(资源遇到问题,请及时私信上传者)
2.虚拟产品一经售出概不退款(资源遇到问题,请及时私信上传者)
版权申诉
0 下载量 170 浏览量
2024-05-12
22:05:43
上传
评论
收藏 290.66MB ZIP 举报
温馨提示
yolov5算法DMS驾驶员抽烟-打电话-喝水-吃东西检测, 包含5000多张DMS驾驶员抽烟-打电话-喝水-吃东西检测数据集,数据集目录已经配置好,划分好 train,val, test,并附有data.yaml文件,yolov5、yolov7、yolov8,yolov9等算法可以直接进行训练模型,txt格式标签, 数据集和检测结果参考:https://blog.csdn.net/zhiqingAI/article/details/124230743 数据集配置目录结构data.yaml: train: ../train/images val: ../valid/images test: ../test/images nc: 4 names: ['drinking', 'eating', 'mobile use', 'smoking']
资源推荐
资源详情
资源评论
收起资源包目录
YOLOv5算法DMS驾驶员抽烟-打电话-喝水-吃东西分神检测+数据集 (2000个子文件)
README.md 11KB
image21_png.rf.88d1a1230c5b906e02faa20fb0747e93.txt 229B
image21_png.rf.1122945cf71fe20673fba7acffe26ec4.txt 228B
image2431_png.rf.a557b47dec9ac4249afe806fe16829f9.txt 81B
image2434_png.rf.42b323c586dcfbbc82efc3099dde7cb0.txt 81B
image2431_png.rf.fd55d991baa13d9cc33b29738e75c380.txt 81B
image2434_png.rf.e3a8fcc195cd7a1d52c0af930e206c72.txt 81B
image2443_png.rf.9e427296e66baff440259b5dc021c482.txt 76B
image2433_png.rf.f2258171c4342e0a41d36fe7ca429c22.txt 76B
image2432_png.rf.c24e046a205e9425031a4f6287df224d.txt 76B
image2433_png.rf.55737d6b0d354e91b8891c348c261566.txt 76B
image2445_png.rf.2ac6ea3ae90903c0cb4a48a9e1de3f0e.txt 74B
image2446_png.rf.7cddac85a42039d2b1e213ebc440ce6e.txt 73B
image2446_png.rf.1acfcac890435fe0729d84bb4c993a3a.txt 73B
image704_png.rf.b7dc21e5de9d806c93e3e9fd27080b6e.txt 72B
image2440_png.rf.da2b9c0414a444de753a3d3da2805837.txt 71B
image1307_png.rf.5f4de13326413518568515bdc4c7d081.txt 43B
image1731_png.rf.81ed2801a54bdaaa1f6c717ef11400bd.txt 43B
image1307_png.rf.a8c502bf3cf4cade02f85402113b8c71.txt 43B
image863_png.rf.f7b3902e758e48d7c19c35f8acf74dae.txt 43B
image1534_png.rf.468eda74a7553602c1a0792fdc5f0bd2.txt 43B
image1894_png.rf.ad5f715c0758547e4edfc492eb4874b3.txt 43B
image1533_png.rf.e05b51653f51733951645906e427a30a.txt 43B
image1531_png.rf.da527e7792de01798c95d06622b09935.txt 43B
image1532_png.rf.af6cdd6dd4cdd5495fe478e5446507bb.txt 43B
image1731_png.rf.1407f6e1c93cfd3739458c705816c6db.txt 43B
image915_png.rf.b8383030d7d28b6766f8e9cca13bee9e.txt 43B
image2260_png.rf.75a260181a73754e9baaba9877e5fded.txt 43B
image2132_png.rf.f7d233c7d68430c967065701460ebca8.txt 43B
image915_png.rf.5b9bdeb0e2efbd1b01b867a6520a5a61.txt 43B
image1894_png.rf.992a94618082a220d9c46ce12d674d75.txt 43B
image1258_png.rf.ed3490082cd7e9ef5417b3e4d31a1510.txt 43B
image1505_png.rf.f2870f66cfe5205b5c301a1c6aeb9ce6.txt 43B
image2158_png.rf.69f373f6aa6077d724b37b440f200e6a.txt 43B
image1995_png.rf.532d0d6fe2fd651e49abcbea5f867ec0.txt 43B
image2962_png.rf.8b1ffce4b8059bcc3a67f91ad84565a9.txt 43B
image2193_png.rf.3a27650c8f0f3647fddebf0a7dfe0e9d.txt 43B
image428_png.rf.6713954dd375c1dfeab12a972c80783b.txt 43B
image843_png.rf.3bd164d3773d76f1b9bb9334e44046b0.txt 43B
image644_png.rf.4147fc6982b805ab2f9e1647410f348d.txt 43B
image1417_png.rf.3b424dc36d4330f758c96bfd548f3857.txt 43B
image1729_png.rf.5608f623cf60ebc4061f33938ba12378.txt 43B
image1307_png.rf.32d801d65708dde3d014f9c628489c4d.txt 43B
image1258_png.rf.8c481e4b74af23d3ae6e3fb1bf0174a7.txt 43B
image2145_png.rf.b5c3ca9369f1d246b09b087fa1c50eae.txt 43B
image2888_png.rf.8018d03052b28a1233a95338519a937d.txt 43B
image2962_png.rf.e39e94d5a74f76288bf78f59a39cf961.txt 43B
image2888_png.rf.623071c1672bcfcc431f6790b87fcbd4.txt 43B
image2159_png.rf.eb02d42f1d6ed215e7ed92dc389db38d.txt 43B
image1731_png.rf.b09258df05c26e01f8b30474735faad8.txt 43B
image1258_png.rf.9a776b619c2bb9f881184789e2e5167b.txt 43B
image842_png.rf.42b4e42b43db96740e56bfde29299a58.txt 43B
image843_png.rf.985f78b02d238ce66c8dd69f8028c0ba.txt 43B
image2145_png.rf.7e560dfa973d4134b5f62542f42cbf1f.txt 43B
image2132_png.rf.b44ba767c12f27b74fc8dee3cc2fc8b7.txt 43B
image1534_png.rf.593a059442b9ebeac13b41730dc08af4.txt 43B
image1995_png.rf.2fc7b8104791abedf7ff4d72b1f5c583.txt 43B
image2140_png.rf.c6c15d1df4b58b28593917038158e248.txt 42B
image2421_png.rf.d8195df34266642bb7345777bd9d6e4f.txt 42B
image89_png.rf.91bda0189d985861c53725db6e59d0c2.txt 42B
image814_png.rf.b4be1a6ec44040cee2aa2ec3d6ddf0ff.txt 42B
image1990_png.rf.d15535a48ea6526992e777cda65313dc.txt 42B
image2327_png.rf.7b6961e3c6cf30fa26ef6fa37091e771.txt 42B
image1608_png.rf.0788db0295df7723c3724793c4e5dbe8.txt 42B
image2048_png.rf.0a7f02c6760a4236c2be2fbcd47bafe1.txt 42B
image803_png.rf.7aa399f0feca2e79048265e78eae7d87.txt 42B
image947_png.rf.352659c6dce697d0b3d8ec551aa73b28.txt 42B
image3051_png.rf.34c4a827d6ef54192383611b69b0d66b.txt 42B
image717_png.rf.e37d278ecfacdfbf1926d008ec502d66.txt 42B
image2332_png.rf.45fe9ea212fcc9bdd03fa931ef50474a.txt 42B
image2096_png.rf.c31c3d4fbe324e60c650faf56293e63e.txt 42B
image2825_png.rf.13e716129ba4a1a7f708b8293eb43d15.txt 42B
image1316_png.rf.098656525607fc43e0840d224a4a970c.txt 42B
image2678_png.rf.c23904a0fc620f258760aab82ffcef79.txt 42B
image808_png.rf.38a5d54485b72f061783de4529c6dfc1.txt 42B
image2871_png.rf.da860c9f15175ad99981077b0ee3840a.txt 42B
image1218_png.rf.3739272c78246d51f87926501a9516db.txt 42B
image1999_png.rf.beac5abefed27cea897ef32e74a05867.txt 42B
image2794_png.rf.636ae4b37da9d73e8e16da380d85b7e9.txt 42B
image2373_png.rf.ee4b3eedef90cfa3a157249bdd21eca4.txt 42B
image2103_png.rf.a91741107335913326a196cdfa6ce09d.txt 42B
image947_png.rf.dde1a2d88781adad0594ad1a213f9549.txt 42B
image594_png.rf.d6b6329f57de1cd5ffefe025ad8819cc.txt 42B
image1394_png.rf.0d03cc92eb7aa1eff355a475145804ac.txt 42B
image2952_png.rf.10d3808b31323e6787ecf011b9280cc4.txt 42B
image1441_png.rf.75a5e4da683df3f07f74651c0ed81666.txt 42B
image1340_png.rf.bcd4b0b9d61688ded675e99442224cfa.txt 42B
image2031_png.rf.167c3803a8a880efa78c56cd27dedc1c.txt 42B
image1383_png.rf.22dda5bace9aa80d77dd9f3cd693959d.txt 42B
image2313_png.rf.bbab94a28bacf201c0d064442698a749.txt 42B
image2421_png.rf.24e60e3c423ea477923b8334754f7772.txt 42B
image2616_png.rf.6b64542854ba9914330c33da57c841bc.txt 42B
image1869_png.rf.f4441eeec9a8edf54c707899dbbdf759.txt 42B
image2678_png.rf.8e1d5300df985599ab6f8a34f462d2b6.txt 42B
image1266_png.rf.e4c16aecb1b06eb48e46415f89a2cc1a.txt 42B
image717_png.rf.cfb066f98bda6b3e1150716bd6820542.txt 42B
image1218_png.rf.c259c946a07a99ade7411ece2edc6537.txt 42B
image2031_png.rf.2807ff84eb2f0815a1eb0b5cf2a9d71a.txt 42B
image2678_png.rf.f756b9306b3206b5cb176617bab33827.txt 42B
image3062_png.rf.96c9babd4b70b4c42f1058630748e03f.txt 42B
共 2000 条
- 1
- 2
- 3
- 4
- 5
- 6
- 20
资源评论
stsdddd
- 粉丝: 2w+
- 资源: 710
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功